Often, in image classification research, datasets with the richness of ImageNet [1]} are desired. However, the richness of the datasets imposes extraordinary difficulties for researchers to visually find any useful pattern of errors. For example, there are 1000 classes in ImageNet. It is impractical to find any useful pattern in a 1000 by 1000 confusion matrix. To smooth the path towards finding these patterns, without adding complexity to the ImageNet dataset, we “superclass” ImageNet  — "Superclassing ImageNet dataset". Superclassing ImageNet dataset is a hierarchical subset of ImageNet that contains broad superclasses (derived from Wordnet, see Sec. REF ) which each subsume several of the original ImageNet classes (Fig. REF ). Our dataset contains 10 superclasses and each superclass contains 7 to 116 subclasses, for a total of 253 subclasses. Thus, by adopting this hierarchical subdivision, we initially only navigate through 10 superclasses instead of the 1000 classes in the original ImageNet dataset. We found useful patterns in a 10 by 10 confusion matrix (Fig. REF ) and in the summary of classification accuracies for all subclasses within each superclass (Fig. REF ). We highlight two observations from training convolutional neural networks on this dataset:
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